Probabilistic Methods for Bioinformatics: With an Introduction to Bayesian Networks

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9780123704764: Probabilistic Methods for Bioinformatics: With an Introduction to Bayesian Networks

The Bayesian network is one of the most important architectures for representing and reasoning with multivariate probability distributions. When used in conjunction with specialized informatics, possibilities of real-world applications are achieved. "Probabilistic Methods for Financial and Marketing Informatics" covers two of the most important applications of informatics and concentrates on approaches to solving realistic business problems. This book provides applications of informatics to areas such as managerial options and decision making, investment science, marketing, and data mining, concentrating on the probabilistic and decision-theoretic approaches to informatics, emphasizing the use of Bayesian networks. Rather than dwelling on rigor, algorithms, and proofs of theorems, the authors focus on problem solving and practical applications. Included in this book are ample examples and exercises throughout, as well as six chapters that each walk through pragmatic situations. In many cases, solutions are expanded, as the authors discuss their final implementation using the software package Netica. Features of this title are as mentioned below. It features unique coverage of probabilistic reasoning topics applied to business problems, including advertising, market basket analysis, venture capital decision making, operational risk measurement, bankruptcy prediction, and investment science. It shares insights about when and why probabilistic methods can and cannot be used effectively. It provides complete review of Bayesian networks and probabilistic methods for those IT professionals new to probabilistic informatics. It presents unique coverage of probabilistic reasoning methods applied to bioinformatics data - those methods that are likely to become the standard analysis tools for bioinformatics. It offers complete review of Bayesian networks and probabilistic methods with a practical approach.

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"Probabilistic Methods for Financial and Marketing Informatics makes important and novel contributions to understanding investment strategies and should be welcomed by practitioners and researchers alike" --Tony Volpon, Strategist/Chief Economist, CM Capital Markets "Richard Neapolitan's new book with Xia Jiang on Bayes nets and its applications to finance and marketing is a must-read for professionals in the finance and marketing communities. The relatively new technique of Bayes nets has great potential for managing uncertainty in all domains. But applications of this technique in finance and marketing have not been widely reported to date. This new book fills this gaping void at a level most professionals can easily understand. Congratulations to Neapolitan and Jiang for this excellent addition to the literature on Bayes nets!" --Prakash P. Shenoy, University of Kansas

About the Author:

Richard E. Neapolitan is professor and Chair of Computer Science at Northeastern Illinois University. He has previously written four books including the seminal 1990 Bayesian network text Probabilistic Reasoning in Expert Systems. More recently, he wrote the 2004 text Learning Bayesian Networks, the textbook Foundations of Algorithms, which has been translated to three languages and is one of the most widely-used algorithms texts world-wide, and the 2007 text Probabilistic Methods for Financial and Marketing Informatics (Morgan Kaufmann Publishers).

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Book Description ELSEVIER SCIENCE TECHNOLOGY, United States, 2009. Hardback. Book Condition: New. 236 x 190 mm. Language: English . Brand New Book. The Bayesian network is one of the most important architectures for representing and reasoning with multivariate probability distributions. When used in conjunction with specialized informatics, possibilities of real-world applications are achieved. Probabilistic Methods for BioInformatics explains the application of probability and statistics, in particular Bayesian networks, to genetics. This book provides background material on probability, statistics, and genetics, and then moves on to discuss Bayesian networks and applications to bioinformatics. Rather than getting bogged down in proofs and algorithms, probabilistic methods used for biological information and Bayesian networks are explained in an accessible way using applications and case studies. The many useful applications of Bayesian networks that have been developed in the past 10 years are discussed. Forming a review of all the significant work in the field that will arguably become the most prevalent method in biological data analysis, it has unique coverage of probabilistic reasoning methods applied to bioinformatics data - those methods that are likely to become the standard analysis tools for bioinformatics. It shares insights about when and why probabilistic methods can and cannot be used effectively. It includes a complete review of Bayesian networks and probabilistic methods with a practical approach. Bookseller Inventory # AA59780123704764

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Book Description ELSEVIER SCIENCE TECHNOLOGY, United States, 2009. Hardback. Book Condition: New. 236 x 190 mm. Language: English . Brand New Book. The Bayesian network is one of the most important architectures for representing and reasoning with multivariate probability distributions. When used in conjunction with specialized informatics, possibilities of real-world applications are achieved. Probabilistic Methods for BioInformatics explains the application of probability and statistics, in particular Bayesian networks, to genetics. This book provides background material on probability, statistics, and genetics, and then moves on to discuss Bayesian networks and applications to bioinformatics. Rather than getting bogged down in proofs and algorithms, probabilistic methods used for biological information and Bayesian networks are explained in an accessible way using applications and case studies. The many useful applications of Bayesian networks that have been developed in the past 10 years are discussed. Forming a review of all the significant work in the field that will arguably become the most prevalent method in biological data analysis. Bookseller Inventory # AA59780123704764

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